1. Field of the Invention
The present invention relates to image pattern classification and recognition, and more particularly to an apparatus and a method for classifying and recognizing image patterns, irrespective of scale and rotation transformation, using an image input unit for transforming an image of an object into a rotation and scale invariant form and using an artificial neural network.
2. Description of the Prior Art
Recently, an increased demand for automated and manless systems in factories has resulted in a strong need for an apparatus for classifying and recognizing image data. In particular, when an object or an image input unit used is translated, an image of the object input in the image input unit is transformed in scale and rotation. In this case, accordingly, a transform invariant recognition is needed. In this regard, there have been proposed devices for transforming images involving various transformations in scale and rotation into a certain invariant form, and thereby recognizing the result.
Referring to FIG. 1, illustrated is an example of a conventional case as mentioned above. As shown in FIG. 1, the conventional apparatus includes a complex-log mapping unit 11, a Fourier transform unit 13 and a multilayer perceptron unit 15. In this case, an input image is transformed into a scale and rotation invariant image using the scale and rotation invariance of complex-log mapping. A Fourier transform is also carried out to correct possible positional translation of an output of complex-log mapping. The transformed result is classified and recognized by a neural network. The conception of the complex-log mapping unit is described in "Form-Invariant, Topological Mapping Strategy for 2D shape Recognition", by L. Massone et al., reference being made to a "retino-cortical mapping".
However, this conventional case involves an additional computation for achieving the Fourier transform. As a result, it has the disadvantages of a very difficult parallel processing and a high network complexity.